Adverse events of special interest in clinical trials of rheumatoid arthritis, psoriatic arthritis, ulcerative colitis and psoriasis with 37 066 patient-years of tofacitinib exposure
Bibliographic record
Abstract
OBJECTIVES: To analyse adverse events (AEs) of special interest across tofacitinib clinical programmes in rheumatoid arthritis (RA), psoriatic arthritis (PsA), ulcerative colitis (UC) and psoriasis (PsO), and to determine whether the incidence rates (IRs; unique patients with events per 100 patient-years) of these events are consistent across diseases. METHODS: The analysis included data from patients exposed to ≥1 dose of tofacitinib in phase 1, 2, 3 or 3b/4 clinical trials and long-term extension (LTE) studies (38 trials) in RA (23 trials), PsA (3 trials), UC (5 trials) and PsO (7 trials). All studies were completed by or before July 2019, except for one ongoing UC LTE study (data cut-off May 2019). IRs were obtained for AEs of special interest. RESULTS: 13 567 patients were included in the analysis (RA: n=7964; PsA: n=783; UC: n=1157; PsO: n=3663), representing 37 066 patient-years of exposure. Maximum duration of exposure was 10.5 years (RA). AEs within the 'infections and infestations' System Organ Class were the most common in all diseases. Among AEs of special interest, IRs were highest for herpes zoster (non-serious and serious; 3.6, 1.8, 3.5 and 2.4 for RA, PsA, UC and PsO, respectively) and serious infections (2.5, 1.2, 1.7 and 1.3 for RA, PsA, UC and PsO, respectively). Age-adjusted and sex-adjusted mortality ratios (weighted for country) were ≤0.2 across cohorts. CONCLUSIONS: The tofacitinib safety profile in this analysis was generally consistent across diseases and with longer term follow-up compared with previous analyses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".